Bridging the clinical gaps: genetic, epigenetic and transcriptomic biomarkers for
the early detection of lung cancer in the post-NLST era
Brothers JF*, Hijazi K*, Mascaux C, El-Zein RA, Spitz MR, Spira A
Abstract
Lung cancer is the leading cause of cancer death worldwide in part due to our inability
to identify which smokers are at highest risk and the lack of effective tools to detect the
disease at its earliest and potentially curable stage. Recent results from the National
Lung Screening Trial have shown that annual screening of high-risk smokers with lowdose helical computed tomography of the chest (CT chest) can reduce lung cancer
mortality. However, molecular biomarkers are needed to identify which current and
former smokers would benefit most from annual CT scan screening in order to reduce
the costs and morbidity associated with this procedure. Additionally, there is an urgent
clinical need to develop biomarkers that can distinguish benign from malignant lesions
found on CT chest given its very high false positive rate. This review highlights recent
genetic, transcriptomic and epigenomic biomarkers that are emerging as tools for the
early detection of lung cancer both in the diagnostic and screening setting.
Keywords
Introduction
Lung cancer is the leading cause of cancer death in both men and women in the United
States and the world, causing more than 1 million deaths per year [1-4]. The global
cancer burden in annual cases is projected to double by 2050, and lung cancer is
expected to remain the leading cause of all cancer deaths during that time. Cigarette
smoke remains the main risk factor for lung cancer, with 85-90% percent of lung cancer
cases in the US occurring in current or former smokers. However, only 10-20% of heavy
smokers develop lung cancer [5]. While smoking cessation gradually reduces the risk of
lung cancer, the majority of new lung cancer cases occur in former smokers. The high
mortality in patients with lung cancer (80-85% in five years) results, in part, from our
inability to predict which of the 100 million current and former smokers in the United
States are at greatest risk for developing lung cancer and from the lack of effective tools
to diagnose the disease at an early stage [6].
Recent results published from the National Lung Screening Trial have shown that
screening high risk smokers (based on age and cumulative exposure to tobacco smoke)
with low-dose helical computed tomography can lead to a reduction in both lung cancer
mortality (by 20.0%) and all-cause mortality (by 6.7%) compared to standard
radiographic screening. However, 39.1% of all participants in the low-dose CT arm of
the trial had at least one positive screen concerning for lung cancer, and 96.4% of these
initial positive screening represented false positives for lung cancer [7]. This
overabundance of false positives could lead to higher screening costs and unnecessary
invasive procedures on many smokers who do not actually have lung cancer. Thus,
there is a critical need to develop biomarkers that can 1) determine which of the
frequently detected lung nodules on CT scan are malignant (i.e. diagnostic markers)
and 2) how to further define the large high-risk population that would be eligible for
screening by CT to increase the efficacy of screening and to reduce the cost and
morbidity associated with it (i.e. screening markers; figure 1)
The sequencing of the human genome, together with the technological advances that
enabled this accomplishment have ushered in a new era of molecular biomarker
development that promises to help address these unmet needs. This review will
summarize recent genetic, transcriptomic and epigenomic biomarkers that are emerging
as tools for the early detection of lung cancer (figure 2), both in the diagnostic and the
screening setting (prognostic and predictive biomarkers will not be covered). The review
will focus on genome-wide studies in clinical biospecimens (no animal models or cell
line studies) that leverage these emerging high-throughput technologies, and will review
commonality of variants between lung cancer and chronic obstructive airways disease.
While there are a number of promising metabolic and proteomic biomarkers for early
lung cancer detection, these fall outside the scope of this review [8].
GWAS studies to identify genetic risk factors for lung cancer (Table 1)
Initial genome-wide associations in lung cancer robustly implicated SNPs spanning the
chr.15q25 region encoding the gene cluster of nicotinic receptors, CHRNA3/A5/B4 [912]. Subsequent multi-investigator consortia analyses confirmed the association of
SNPs spanning this region with heavy smoking, nicotine dependence, craving and
related endophenotypes [11,13,14]. Saccone et al. [13] conducted a meta-analysis
across 34 datasets of European-ancestry subjects, including a diverse group of 38,617
smokers and demonstrated that rs16969968, a nonsynonymous coding polymorphism
of the CHRNA5 gene, correlated highly significantly with smoking behavior (OR = 1.33,
p = 5.96×10−31). Three other large smoking genetics consortia confirmed this locus as
that most associated with smoking quantity [11,14,15].
Therefore the challenging question was the degree to which the associations between
these chr15q25 variants and lung cancer were due to their effects on smoking intensity,
rather than a direct carcinogenic effect. The lung cancer association, though statistically
robust, and initially not altered by adjustment for smoking, increasingly appears to be
mediated through smoking. However, there is still uncertainty as to the degree with
which the risk for lung cancer is mediated through the genetic risk to smoking. Saccone
et al. [13] showed that locus 1 was associated with lung cancer even when controlling
for amount smoked per day (p = 1.99×10−21, OR = 1.31), suggesting possible direct
genetic effects of locus 1 on this cancer, at least in the presence of smoking. Spitz et al.
[16] noted that the lung cancer risk associated with the variant genotype was highest in
lightest smokers (<20 cigarettes per day) and younger patients (<61 years) arguing for a
role for genetic susceptibility in these lesser exposed groups. Furthermore, they [16]
were not able to implicate this locus as a risk factor in other smoking-related cancers
(bladder and renal), suggesting genetic effects on both smoking behavior and lung
cancer risk.
Wang et al. [17] demonstrated that each copy of Chr 15q risk alleles at rs12914385 and
rs8042374 was associated with increased cigarette consumption of 1.0 and 0.9
cigarettes per day, respectively, and concluded that these modest differences in
smoking behavior were sufficient to account for the 15q25 association with lung cancer
risk. However it could also be argued that cigarettes per day is not a sufficient proxy for
carcinogen exposure [18].
Troung et al. [19] used data from twenty-one case-control studies (9 in North America, 8
in Europe and 4 in Asia) and replicated the association between chromosome 15q25
SNPs and lung cancer risk in white ever smokers (OR=1.26 [1.21-1.32], p=1.10-26) and
also confirmed that this association was higher at younger age of onset (p-trend=0.002),
while no association was found in never smokers or in Asians. Spitz et al. [16] found no
elevated risk associated with these variants in over 547 lifetime never smoking lung
cancer cases. Subsequent meta-analyses of never smokers with lung cancer (Galvan et
al. [20] in >1000 never-smoker cases and >1800 controls and Wang et al. [17] in 2,405
cases and 7,622 controls, replicated the lack of any statistically significant association
with this locus in never smokers.
Other top hits identified in the GWAS have also been replicated. A number of welldesigned GWAS and meta-analysis have implicated variants at the 5p15.33 locus in
cancer risk at several different sites, including lung cancer in both whites and Asians
[21]. Truong et al. [19] confirmed the significant association in Whites for rs2736100 in
the chromosome 5p15 locus. Both Troung [19] and Landi [12] noted a histology-specific
role of rs2736100 in adenocarcinoma. This locus was also recently implicated in lung
cancer risk in African Americans[22]. There is biologic plausibility for this finding since
mean relative telomere length was associated with four genetic variants of the hTERT
gene, including rs2736100 [23], and TERT gene amplification is responsible for TERT
mRNA overexpression in a majority of lung adenocarcinomas [24]. Cleft Lip and Palate
Transmembrane Protein 1-Like (CLPTM1L) gene also resides in this region of
chromosome 5 for which copy number gain has been found to be the most frequent
genetic event in early stages of non-small cell lung cancer. James et al. [25]
demonstrated increased CLPTM1L expression in lung adenocarcinomas and protection
from genotoxic stress-induced apoptosis and concluded that anti-apoptotic CLPTM1L
function could be another mechanism of susceptibility to lung tumorigenesis. A third
region implicated by GWAS in susceptibility to lung cancer in Caucasians is the HLA
region at chromosome 6p21 [10,26].
The association with SNPs in the 5p15 and 15q25 regions was confirmed in a Korean
population with similar magnitude of effect as reported for other ethnic groups, but there
was no association with the 6p locus [27]. Likewise, the effect of the 5p15 SNP was
significant only for adenocarcinoma. Truong [19] noted no effect for the chr15q locus,
but replicated the association with the 5p locus in Asians. A Japanese study [28]
confirmed the finding at 5p15.33. There have been several GWAS in Chinese
populations. Hu et al. [29] replicated findings of significance in both 3q28 (TP53) and at
the 5p13 locus. They also reported significance at two additional loci, 12q12 and 22q12
(Table 1). In an attempt to identify additional susceptibility loci in Chinese lung cancer
patients, Dong et al. [30] reported genome-wide significance for three additional lung
cancer susceptibility loci in a Chinese population at 10p14 (close to GATA3), 5q32 in
PPP2R2B-STK32A-DPYSL3, and 20q13.2 in CYP24A1. They also found additional
associations for rs247008 at 5q31.1 (IL3-CSF2-P4HA2), and rs9439519 at 1p36.32
(AJAP1-NPHP4). There was suggestive evidence for interactions with smoking dose.
Jin et al. [31] noted that genetic variants at 6p21.1 and 7p15.3 were associated with risk
of multiple cancers in Han Chinese, including lung cancer. Finally Shi et al. [32] reported
that a locus on RAD52, involved in DNA double-strand break repair and homologous
recombination influenced risk of squamous cell lung cancer but not other cell types.
It is not unlikely that many more common variants can be anticipated to contribute to
lung cancer risk, although with effect sizes too small to reach significance in genomewide analyses. It has been argued that there are diminishing returns in predicting
disease risk from common marker SNPs, and greater effort should be spent to
investigate functional relevance of the GWAS findings. For example, evaluating the
effect that SNP variation has upon expression and activity of nicotinic receptors can be
explored by taking advantage of animal and cellular models of CHRNA3 and CHRNA5
knock out animals, [33,34]. Studies of cell lines and primary lung cancers can provide
insights into the effects of these variants on proliferation and apoptosis; one such study
suggested a role of a proteosome gene in this region beyond the effects of nicotinic
receptors [35]. Emerging metabolomic markers may provide useful biomarker
dosimeters of smoking damage relative to carcinogenesis. Certainly, multiple strategies
are needed to further tease apart these complex relationships [18].
Overlap in Genetic Risk Factors for Lung Cancer and Chronic Obstructive
Pulmonary Disease
Lung cancer and chronic obstructive pulmonary disease (COPD) result from the
combined effects of smoking exposure and genetic susceptibility. Tobacco smoke
exposure has been responsible for 80% of lung cancers, however only 15-20% of
chronic smokers develop lung cancer or COPD. Approximately, 50-90% of smokers with
lung cancer also have COPD. Studies have shown that COPD is an independent risk
factor for lung cancer among Caucasians and African Americans, conferring a 4-6 fold
increased risk. Over the past few years, several lung cancer risk models have been
developed [36-40] some of which included pulmonary diseases such as COPD and
pneumonia. Consistently, the inclusion of COPD in the models leads to improvement of
the discriminatory power and good calibration [41]. Currently the model with the highest
discriminatory power reported to date is the extended Prostate, Lung, Colorectal, and
Ovarian (PLCO) lung cancer risk model [37] which also included COPD. This dual
susceptibility indicates a link between the processes that induce COPD and lung
cancer.
Results from recent genome-wide association studies suggest a possible overlap in the
genetic risk factors predisposing smokers to lung cancer and COPD. Several regions in
the genome have been identified that are associated with lung cancer and/or COPD
including chromosome 1q21, 4q22, 4q24, 4q31, 5p15, 5q32, 6p21, 6q24, 15q25, and
19q13 [9,10,41-49]. Several important genes mapping to those regions have also been
identified as significant players in the lung cancer and/or COPD pathogenesis (Table 1)
and many of these loci overlap. For example, a variant in the FAM13A gene has been
reported to have a protective effect in COPD and lung cancer [49]. The CHRNA3/5
(15q25) was reported to be associated with both COPD and lung cancer [10,48,49]
through its effects on both smoking exposure and COPD. Using mediation analysis,
Wang et al. [50] reported that COPD is a mediating phenotype that could partially
explain the effect of smoking exposure on lung cancer. These findings suggest the
presence of shared susceptibility mechanisms for these two smoking related diseases.
Such susceptibility may also be mediated through receptors expressed on the bronchial
epithelium that implicate molecular pathways underlying both COPD and lung cancer
[51]. To date, most of the lung cancer and COPD genetic studies have been conducted
independent of each other, which in turn contribute to overlooking the mediating effect
of one disease over the other [52].
Epigenetic screening and diagnostic markers for lung cancer (Table 2a)
Epigenetics is classically defined as the study of changes in downstream phenotypes or
gene expression that cannot be attributed to changes in DNA and is heritable. Another
refined definition is that epigenetics are structural changes in chromosomal regions that
are not related to changes in DNA that mark altered activity states [53]. Two major types
of epigenetic regulation are DNA methylation and histone modification, both of which
are known to modulate gene expression. Given that the abundance of molecular
biomarkers in this field have been DNA methylation based, this section will focus on
DNA methylation studies that hold the potential to impact early lung cancer detection.
DNA methylation is an epigenetic mechanism marked by the joining of a methyl group
to a cytosine base to form 5-methylcytosine, typically at a CpG dinucleotide near or
within a CpG island. When CpG dinucleotides are methylated to a high degree in the
promoter region of a gene, that gene’s expression is usually downregulated as a result.
This is one way that cells can regulate which genes are expressed (figure 2) and is a
mechanism utilized during cell and tissue differentiation during development [54].
Aberrant hypermethylation of oncogenes or hypomethylation of tumor suppressor genes
is one way that transcriptional regulation can spiral out of control in cancer cells [55].
Genome-wide methylation profiling has been used to identify altered methylation
patterns in lung cancer tissue (including genes such as CDKN2A, RASSF1A, ARHI,
MGMT, and RARβ) [56,57], but so far only one larger scale study has shown the
possibilities of identifying methylation biomarkers for the diagnostic or screening setting
in noninvasive biospecimens utilizing microarray based technologies [58]. The vast
majority of current methylation studies that could be useful for screening and diagnostic
tests remain at a candidate gene or gene panel level analysis.
Belinsky et al. [59] originally identified the hypermethylation of CDKN2A in lung tumors
but within the same study also examined the sputum of 33 people who smoked. In this
small initial study, 8 patients had sputum with methylated CDKN2A detected by
Methylation-specific PCR (MSP). Of those, 3 were diagnosed with lung cancer at the
time of sputum collection and one other would develop lung cancer a year later [59].
Work on identifying CDKN2A, as well as MGMT, as a measure of cancer risk and
diagnosis was expanded in a 21 patient study of matched sputum and SCC samples as
well as sputum samples from 32 patients evaluated for possible lung cancer. This study
was able to significantly improve cancer detection and risk using the methylation status
of the two genes compared to cytology alone. More importantly, these genes were
aberrantly methylated up to 3 years before diagnosis [60]. By looking at the sputum of
lung cancer surviving smokers, cancer-free smokers, and never smokers, then adjusting
for age and smoking duration, MGMT, RASSF1A, DAPK, and PAX5α were also
identified as being significantly differently methylated in the lung cancer survivors
indicating that aberrant methylation of a panel of candidate genes could identify patients
with higher risk of lung cancer [61]. Other genes that have been identified in the sputum
with aberrant methylation associated with increased risk for lung cancer include
ASC/TMS1 [62], GATA4, GATA5, and PAX5β [63]. Recently, a larger panel of 31 genes
in the sputum was used to identify signatures of stage I lung cancer that had >70%
accuracy and could predict which smokers had cancer between 3 and 18 months before
clinical diagnosis [64].
Other potential distant sites for assessing lung cancer risk using methylation markers
include the serum, plasma, and blood leukocytes. Based on evidence that DNA from
tumor cells can be found freely in circulating serum [65], Esteller M et al. [66] examined
the serum, normal lung tissue, and tumor tissue from 22 patients with NSCLC. They
found that 73% of patients had serum DNA that reflected hypermethylation events found
in their tumors, specifically using MSP to look at methylation of CDKN2A, MGMT,
DAPK, GSTP1, genes whose aberrant methylation profiles have already been shown to
associate with lung cancer risk or diagnosis [66]. A larger study with a cross-section
case/control design looked at the serum from 200 patients, 91 of which had lung cancer,
100 with nonmalignant lung disease, and 9 with some other malignant disease. RARβ,
CDKN2A, DAPK, RASSF1A, and MGMT were examined, and the analysis showed that
a patient having methylation of just one gene was 5.08 times likely to have lung cancer
than patients without any methylated genes and that this odds ratio increased with
patients with two or more genes being aberrantly methylated [67]. Overall, just looking
at this limited candidate gene list, almost 50% of patients with lung cancer displayed at
least one case of aberrant methylation in their serum. Other genes with aberrant
methylation in serum DNA have been found to associate with lung cancer risk including
TMEFF2 [68], RUNX3 [69], CDH13 [70] suggesting that many genes in the serum could
signify lung cancer risk and that a larger profile of aberrant methylation could produce a
more accurate biomarker for lung cancer risk. The work by Begum S et al. [71] looking
at methylation profiles of a slightly larger set of 15 genes and then selecting the six best
genes (APC, CDH1, MGMT, DCC, RASSF1A, and AIM1) clearly shows evidence that a
more global methylome approach could lead to an even more sensitive and specific
biomarker of lung cancer risk from serum DNA [71]. Methylation events in plasma,
specifically in CDKN2A, MGMT, and RASSF1A [61], as well as in peripheral blood
leukocytes [58] and lymphocytes [72,73], are promising less invasive sites for assessing
lung cancer risk through measuring DNA methylation differences.
Transcriptomic biomarkers for screening and diagnosing lung cancer (Table 2b)
Gene expression profiling or transcriptomics has been used to delineate disease
classification, improve diagnostic accuracy, identify new molecular targets for drugs and
provide new biological insights into lung cancer . High-throughput technologies, such as
microarray, and sequencing platforms, allow measurement of thousands of genes
simultaneously and look for different pattern changes across subsets that help
characterize a particular physiological state or clinical phenotype. In this section, we will
review the diagnostic and screening transcriptomic biomarkers that have been
developed in the airway and blood of at risk smokers
Airway-based transcriptomic biomarkers for early detection of lung cancer:
A number of transcriptomic biomarkers for the early detection of lung cancer have
leveraged the so called “field cancerization” or “field effect” paradigm in which
abnormalities in gene expression in the normal bronchial mucosa are shared with those
found in the tumor. Two genome-wide gene-expression profiling studies identified
transcriptomic alterations related to smoking and that were found both in the cancer and
in the normal lung tissue [74,75]. The first study analyzed both lung squamous cell
carcinoma (SCC) compared to the normal epithelium of the bronchi and
adenocarcinoma (ADC) as compared to the normal alveolar lung tissue [74]. The
second study focused on SCC and normal bronchial epithelium [75]. Abnormalities in
the normal bronchial tissue that were similar to those identified in the tumor were seen
in tumor suppressor genes and oncogenes, as well a different functions as xenobiotic
metabolism and redox stress, matrix degradation and cell differentiation.
Based on these studies, a number of groups have been using a comparatively easily
available specimen, airway epithelial cells through bronchial brushings, to measure the
changes in gene expression associated with lung cancer. An 80 gene-expression based
biomarker was developed in main stem bronchial airway epithelial cells that can serve
as a both a sensitive and specific biomarker for diagnosing lung cancer among smokers
undergoing bronchoscopy for suspect disease [76]. Importantly, combing the geneexpression biomarker with cytology obtained at bronchoscopy resulted in 95%
sensitivity and 95% NPV, enabling the physician to avoid unnecessary further invasive
procedures in those smokers without lung cancer. Furthermore, the biomarker was
shown to be associated with lung cancer diagnosis independent of clinical and
radiographic risk factors for disease, although the study was limited in terms of the
clinical and radiographic risk factors that were modeled (e.g. COPD, PET scan results
not included) [77]. Later, Blomquist et al. also reported that a pattern of antioxidant and
DNA repair gene expression in normal airway epithelium was associated with lung
cancer [78]. Blomquist et al. identified a signature of 14 genes that discriminates cases
versus controls with an area under the curve of 0.84 and an accuracy of 80%.
Beyond diagnosing lung cancer, airway gene-expression has also been used to identify
molecular pathways that are deregulated in the bronchial airway of smokers with or at
risk for lung cancer [79]. A gene-expression signature of phosphoinositide-3-kinase
signaling pathway was differentially activated in the cytologically-normal bronchial
airway of both smokers with lung cancer and smokers with premalignant airway lesions
[76]. Furthermore, that study found that the PI3K pathway gene-expression signature
reverses back to baseline in those patients whose dysplastic lesions regress upon
treatment with the candidate lung cancer chemoprophylaxis agent myoinositol. As
airway epithelial cell dysplasia is a pre-neoplastic event in lung carcinogenesis, these
data suggest both that PI3K pathway activation is an early and reversible event during
lung carcinogenesis, and more broadly that bronchial airway epithelial cell gene
expression reflects carcinogenic processes that precede the development of frank
malignancy [79]. This data suggests that alterations in airway gene-expression are an
early and potentially reversible event the process of lung carcinogenesis that potentially
can be used to guide personalized approaches to lung cancer chemoprevention.
Leveraging the microarray dataset of airway epithelium from smokers with and without
lung cancer [76] , Wang et al. [80] provided additional insight into the molecular
pathways altered in the airway of smokers with lung cancer. They identified that the
antioxidant response pathway, regulated by the transcription factor NRF2 (nuclear
factor erythroid-derived 2-like 2, or NFE2L2), was downregulated in the airway of
smokers with lung cancer. Furthermore, they identified potential polymorphisms in the
promoter regions of the antioxidant genes that may associate with decreased airway
gene-expression in response to tobacco smoke
With the emergence of next generation sequencing as a more robust tool for
transcriptomic profiling, Beane et al. sequenced the RNA from bronchial airway
epithelial cell brushings obtained during bronchoscopy from healthy never smoker,
current smoker and smokers with and without lung cancer undergoing lung nodule
resection surgery [81]. There was a significant correlation between the RNA-seq gene
expression data and Affymetrix microarray data generated from the same samples (p <
0.001), although the RNA-Seq data detected additional smoking- and cancer-related
transcripts whose expression was not found to be significantly altered when using
microarrays.
Over the past several years, a number of studies have attempted to move
transcriptomic profiling of the airway in at risk smokers to biosamples that are less
invasive and more easily collected in population-based studies. Two separate groups
have demonstrated that the buccal mucosa gene-expression response to smoking
mirrors that seen in the bronchial airway (one study using punch biopsies of the cheek
[82] and the second using buccal scrapings [83]). Both studies were limited to healthy
smokers and did not assess relationship in bronchial and buccal gene-expression within
the same individual. More recently, Zhang et al. [84] demonstrated a strongly
concordant gene-expression response to smoking in matched nasal and bronchial
samples from active smokers. These studies raise the exciting possibility that
buccal/nasal swabs could be used as a surrogate to bronchial brushings for a relatively
non-invasive screening or diagnostic tool for individual susceptibility to smoking-induced
lung diseases. Additionally, the Zhang L et al. [85] group profiled salivary transcriptomes
of recently diagnosed and untreated smoker and non-smoker lung cancer patients and
matched cancer-free controls. The study led to the discovery of seven highly
discriminatory transcriptomic salivary biomarkers with 93.75 % sensitivity and 82.81 %
specificity in the pre-validation sample set. Data suggests that lung cancer
transcriptomic biomarker signatures are present in human saliva, which could be
clinically used to discriminate lung cancer patients from cancer-free control subjects.
Blood-based transcriptomic biomarkers for early detection of lung cancer:
While development of a gene-expression biomarker in blood that can be collected in a
non-invasive manner is highly attractive, studies have been relatively limited by
degradation of circulating mRNA in serum or plasma. However, gene-expression
alterations identified in lung tumors have been identified in circulating white blood cells
by a number of groups. Showe et al. analyzed gene expression in peripheral blood
mononuclear cell samples of current or former smokers with histologically diagnosed
NSCLC tumors [86]. They identified a 29-gene signature that separates patients with
and without lung cancer with 86% accuracy (91% sensitivity, 80% specificity). Accuracy
in an independent validation set was 78% (sensitivity of 76% and specificity of 82%).
The Rotunno M group analyzed gene expression of lung tissue and peripheral whole
blood (PWB) collected using paxgene blood RNA tubes from adenocarcinoma cases
and controls to identify dysregulated lung cancer genes that could be tested in blood to
improve identification of at-risk patients in the future [87]. Zander T. et.al further
investigated the validity of whole-blood based gene expression profiling for detection of
lung cancer patients among smokers from 3 different datasets. They showed that RNAstabilized whole blood samples can indeed be used to develop a gene expression–
based classifier that can be used as a biomarker to discriminate between NSCLC cases
and controls [88].
MiRNA biomarkers for the early detection of lung cancer (Table 2c)
MicroRNAs are recently discovered small molecules that play an important role in
regulating gene expression. These noncoding RNAs, in their final active form, are
usually 22 nucleotides in length and target specific parts or mRNA sequences, usually
found in the 3’ UTR regions of mRNA, which either prevent translation or promote
mRNA degradation, and lead to downregulation of specific genes [89]. Since miRNA are
relatively more stable than mRNA [90], any miRNA profiles of lung cancer risk or
diagnosis are likely to be more accurate when moving from the bench to the clinic. This
review will focus on large-scale miRNA studies that have been performed in airway,
sputum and blood for early lung cancer detection
In bronchial tissue
By global profiling of miRNA in premalignant airway lesions, sixty-nine miRNA were
found to evolve in high-risk patients from a pre-invasive stage to a higher stage in the
multistep process of lung carcinogenesis. The expression profiles of 30 and 15 miRNAs
were able to discriminate low-grade lesions from high-grade ones including or not
invasive carcinoma [91]. While this data suggests that airway miRNA expression may
serve as an early detection biomarker, this study was limited to bronchial biopsies of
premalignant airway lesions, which are relatively invasive. As with the gene-expression
studies outlined above, more microRNA profiles in airway epithelial brushings are
needed to advance the field.
In Sputum
Given the relative stability of miRNA in biological specimens, a number of groups have
explored the utility of miRNA-based biomarkers in sputum samples. Xie Y et al. [92]
showed that miRNA profiles in the sputum could be used to identify non-small cell lung
cancer [92]. More recently, two studies were also able to identify and distinguish miRNA
profiles that could do early detection of SCC [93] or adenocarcinoma [94]. Both studies
included a test set and a validation set. A SCC signature of three miRNAs diagnosed
the presence of a stage I SCC in patients’ sputum with a sensitivity of 73%, a specificity
of 96% an AUC of 0.87 in the test set [95]. The adenocarcinoma signature composed of
four miRNA, detected patients with stage I adenocarcinoma with a specificity of 81%, a
sensitivity of 92% and an AUC of 0.90 [96]. There was no overlap between the two
signatures in sputum. In total seven different miRNAs were identified in these two
signatures and these miRNAs could be risk factors for lung cancer and be used to
diagnose lung cancer.
In blood
The relative stability of miRNA has prompted numerous groups to explore the potential
utility of a blood-based miRNA biomarker for early detection of lung cancer. Most of
these have been specifically looking for circulating miRNA in plasma or serum, whereas
five studies have examined miRNA expression profiles in whole blood [97-101]. Seven
studies analyzed miRNAs expression in serum [102-108], and three in plasma [109111]. Six out of the ten studies included a validation set and the same four studies
described the performance of the test, i.e. sensitivity, specificity and/or AUC
[102,103,105,107,109,110].
Notably, only three studies included samples at earlier time points than diagnosis
[103,104,109], which is required for evaluating miRNAs as a risk or screening
biomarker. Boeri et al. identified miRNA signatures that predict lung cancer
development and prognosis [109]. They analyzed miRNA expression in 38 patients with
lung cancer from the INT-IEO cohort (training set) and 53 from the MILD trial (validation
set). With a signature composed of a ratio of 15 miRNAs, they could predict risk of lung
cancer in patient with nodules in the CT screening with a sensitivity of 80%, a specificity
of 90% and an area under the curve (AUC) of 0.85. A signature composed of a ratio of
13 miRNAs was able to diagnose lung cancer in undermined CT screened lung nodules
with a sensitivity of 75%, a specificity of 100% and an AUC of 0.88. The study of Boeri
et al. [109] is the only work so far addressing directly the role of biomarkers for the
work-up of CT screened nodules.. In addition to requiring further prospective validation,
this study might be too complex to apply in practice. Another more recent study by
Bianchi F et al. [103] has identified a 34 miRNA profile that could predict which
asymptomatic high-risk individuals were likely to develop a lung cancer with an
accuracy of 80%. Among the 5203 high-risk individuals studied, 93 went on to be
diagnosed with NSCLC in the first two years of screening. Serum before surgery in 59
of these 93 patients and 69 matched control patients enrolled in the same study. Using
a training set and test set they were able to identify the 34 miRNA biomarker, one which
can better identify lung cancer risk and be more properly used as a screening test [103].
Keller et al. [98] have applied next generation miRNA sequencing to blood to identify
miRNAs associated with lung cancer. In this study, they did ultra deep (~25 million
reads per sample of small RNA) sequencing of white blood samples from 10 patients
with NSCLC and 10 health individuals. They were able to identify seven entirely novel
miRNAs (not annotated in miRBase at the time) that were significantly altered in cancer
patients, [98]. This small study shows the power that miRNA-seq could play in
discovering entirely new risk factors or biomarkers for lung cancer and hopefully more
studies with larger groups of patients will be pursued in the future.
Free circulating DNA biomarkers
Conclusions/Future Directions:
As CT screening programs for lung cancer proliferate in the post-NLST era, there is an
urgent and growing need to develop and validate biomarkers that can both help identify
those smokers at highest risk who are most likely to benefit from screening and help
distinguish benign from malignant lesions found on chest imaging. The recent advances
in genetics and genomics have ushered in an era of genome-wide studies aimed at
identifying molecular biomarkers for diagnosis and risk for lung cancer. While a number
of promising genetic, transcriptomic and epigenomic markers have been identified as
detailed above, we have yet to see translation from biomarker discovery to clinical
application.
A review of these studies reveals several important limitations that will need to be
addressed in the coming years if the field is to advance and have a clinical impact. First,
molecular biomarkers discussed in this review will need to be validated in multicenter
trials on independent cohorts to demonstrate the validity and generalizability of the
biomarker. Importantly, the biomarkers will need to be validated in the clinical setting in
which they will be applied. This latter caveat is best addressed at the biomarker
development stage, where molecular markers are identified among clinical specimens
that reflect the ultimate clinical application (e.g. for diagnostic markers, using specimens
collected prior to lung cancer diagnosis among cases and controls who present with
suspicion of disease). In order to have clinical utility, these molecular markers will need
to demonstrate performance metrics that would alter clinical decision making (e.g.
having a very high NPV in the diagnostic setting). They will further need to demonstrate
that they provide information about cancer risk/diagnosis that is independent of clinical
and radiographic risk factors that have been well established for disease. The ultimate
translation to the clinic, however, will require transitioning to analytical platforms that
can be readily applied in the clinic in order to facilitate physician adoption as part of their
standard of care.
Competing interests
Authors’ contributions
Authors’ information
Acknowledgements
FIGURES:
Figure 1: An overview of clinically unmet needs that exist following the National Lung Screening Trial. While there is a reduction in both lung cancer
mortality and all-cause mortality when using low-dose CT, there are still two major unmet needs highlighted by the trial. The first unmet need is to limit the
number of people who are screened with low-dose CT to those with the highest risks. Genetic, transcriptomic, and epigenetic screening biomarkers could meet
this need by identifying smokers with the highest likelihood of developing lung cancer. The second unmet need comes from the high number of nodules
identified by CT, which are false positives for lung cancer. Early diagnostic biomarkers could play a key role in identifying which nodules are likely to be
cancerous before sending patients into surgery.
Figure 2: Biological rationale for addressing clinical issues by using
upstream early events as genomic biomarkers that ultimately lead to lung
cancer phenotypes. The diagram highlights the early upstream markers for
diagnosing/screening of lung cancer far in advance to development of clinically
evident invasive carcinomas which are mainly driven by genetic, epigenetic and
transcriptomic damages.
Table 1: Regions and genes associated with Lung Cancer and/or COPD
Table 2: Methylation-, gene-expression-, and miRNA-based biomarkers
for risks and early detection of lung cancer
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